{Reference Type}: Journal Article
{Title}: Gaussian filter facilitated deep learning-based architecture for accurate and efficient liver tumor segmentation for radiation therapy.
{Author}: Lin H;Zhao M;Zhu L;Pei X;Wu H;Zhang L;Li Y;
{Journal}: Front Oncol
{Volume}: 14
{Issue}: 0
{Year}: 2024
{Factor}: 5.738
{DOI}: 10.3389/fonc.2024.1423774
{Abstract}: <B>UNASSIGNED: </B>Addressing the challenges of unclear tumor boundaries and the confusion between cysts and tumors in liver tumor segmentation, this study aims to develop an auto-segmentation method utilizing Gaussian filter with the nnUNet architecture to effectively distinguish between tumors and cysts, enhancing the accuracy of liver tumor auto-segmentation.<BR><B>UNASSIGNED: </B>Firstly, 130 cases of liver tumorsegmentation challenge 2017 (LiTS2017) were used for training and validating nnU-Net-based auto-segmentation model. Then, 14 cases of 3D-IRCADb dataset and 25 liver cancer cases retrospectively collected in our hospital were used for testing. The dice similarity coefficient (DSC) was used to evaluate the accuracy of auto-segmentation model by comparing with manual contours.<BR><B>UNASSIGNED: </B>The nnU-Net achieved an average DSC value of 0.86 for validation set (20 LiTS cases) and 0.82 for public testing set (14 3D-IRCADb cases). For clinical testing set, the standalone nnU-Net model achieved an average DSC value of 0.75, which increased to 0.81 after post-processing with the Gaussian filter (P<0.05), demonstrating its effectiveness in mitigating the influence of liver cysts on liver tumor segmentation.<BR><B>UNASSIGNED: </B>Experiments show that Gaussian filter is beneficial to improve the accuracy of liver tumor segmentation in clinic.